Desoiling Dataset: Restoring Soiled Areas on Automotive Fisheye Cameras

Michal Uricar, Jan Ulicny, Ganesh Sistu, Hazem Rashed, Pavel Krizek, David Hurych, Antonin Vobecky, Senthil Yogamani; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0

Surround-view cameras became an integral part of autonomous driving setup. Being directly exposed to harsh environmental settings, they can get soiled easily. When cameras get soiled, the degradation of performance is usually more dramatic compared to other sensors. Having this on mind, we decided to design a dataset for measuring the performance degradation as well as to help constructing classifiers for soiling detection, or for trying to restore the soiled images, so we can increase the performance of the off-the-shelf classifiers. The proposed dataset contains 40+ approximately 1 minute long video sequences with paired image information of both clean and soiled nature. The dataset will be released as a companion to our recently published dataset [14] to encourage further research in this area. We constructed a CycleGAN architecture to produce de-soiled images and demonstrate 5% improvement in road detection and 3% improvement in detection of lanes and curbs.